Overview

Dataset statistics

Number of variables16
Number of observations254974
Missing cells3345
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.4 MiB
Average record size in memory129.2 B

Variable types

Text2
Categorical3
Numeric11

Alerts

countries_fr has a high cardinality: 536 distinct valuesHigh cardinality
brands has a high cardinality: 45792 distinct valuesHigh cardinality
countries_fr is highly imbalanced (82.1%)Imbalance
brands has 3280 (1.3%) missing valuesMissing
cholesterol_100g is highly skewed (γ1 = 293.6387501)Skewed
code has unique valuesUnique
additives_n has 82104 (32.2%) zerosZeros
energy_100g has 6091 (2.4%) zerosZeros
salt_100g has 36339 (14.3%) zerosZeros
sodium_100g has 36343 (14.3%) zerosZeros
fiber_100g has 124820 (49.0%) zerosZeros
sugars_100g has 50158 (19.7%) zerosZeros
fat_100g has 78545 (30.8%) zerosZeros
saturated_fat_100g has 96736 (37.9%) zerosZeros
cholesterol_100g has 200361 (78.6%) zerosZeros
nutrition_score_uk_100g has 12521 (4.9%) zerosZeros
nutrition_score_fr_100g has 11704 (4.6%) zerosZeros

Reproduction

Analysis started2024-06-07 15:45:32.095533
Analysis finished2024-06-07 15:45:57.994357
Duration25.9 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

code
Text

UNIQUE 

Distinct254974
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:58.201522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length30
Median length13
Mean length12.825159
Min length2

Characters and Unicode

Total characters3270082
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254974 ?
Unique (%)100.0%

Sample

1st row0000000004530
2nd row0000000004559
3rd row0000000016087
4th row0000000016094
5th row0000000016100
ValueCountFrequency (%)
0000000004530 1
 
< 0.1%
0000000017497 1
 
< 0.1%
0000000032070 1
 
< 0.1%
0000000018449 1
 
< 0.1%
0000000016087 1
 
< 0.1%
0000000016094 1
 
< 0.1%
0000000016100 1
 
< 0.1%
0000000016117 1
 
< 0.1%
0000000016124 1
 
< 0.1%
0000000016193 1
 
< 0.1%
Other values (254964) 254964
> 99.9%
2024-06-07T17:45:58.602529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 836904
25.6%
1 361910
11.1%
2 306344
 
9.4%
3 303811
 
9.3%
7 265991
 
8.1%
4 260417
 
8.0%
5 250756
 
7.7%
8 245215
 
7.5%
6 237109
 
7.3%
9 201625
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3270082
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 836904
25.6%
1 361910
11.1%
2 306344
 
9.4%
3 303811
 
9.3%
7 265991
 
8.1%
4 260417
 
8.0%
5 250756
 
7.7%
8 245215
 
7.5%
6 237109
 
7.3%
9 201625
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3270082
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 836904
25.6%
1 361910
11.1%
2 306344
 
9.4%
3 303811
 
9.3%
7 265991
 
8.1%
4 260417
 
8.0%
5 250756
 
7.7%
8 245215
 
7.5%
6 237109
 
7.3%
9 201625
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3270082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 836904
25.6%
1 361910
11.1%
2 306344
 
9.4%
3 303811
 
9.3%
7 265991
 
8.1%
4 260417
 
8.0%
5 250756
 
7.7%
8 245215
 
7.5%
6 237109
 
7.3%
9 201625
 
6.2%

countries_fr
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct536
Distinct (%)0.2%
Missing65
Missing (%)< 0.1%
Memory size2.5 MiB
États-Unis
167716 
France
61464 
Suisse
 
8416
Allemagne
 
4464
Espagne
 
2924
Other values (531)
 
9925

Length

Max length211
Median length10
Mean length8.9393627
Min length4

Characters and Unicode

Total characters2278724
Distinct characters120
Distinct categories9 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)0.1%

Sample

1st rowÉtats-Unis
2nd rowÉtats-Unis
3rd rowÉtats-Unis
4th rowÉtats-Unis
5th rowÉtats-Unis

Common Values

ValueCountFrequency (%)
États-Unis 167716
65.8%
France 61464
 
24.1%
Suisse 8416
 
3.3%
Allemagne 4464
 
1.8%
Espagne 2924
 
1.1%
Royaume-Uni 1620
 
0.6%
France,Suisse 1066
 
0.4%
Russie 805
 
0.3%
Belgique 598
 
0.2%
Australie 502
 
0.2%
Other values (526) 5334
 
2.1%

Length

2024-06-07T17:45:58.765095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
états-unis 167717
65.7%
france 61464
 
24.1%
suisse 8416
 
3.3%
allemagne 4464
 
1.7%
espagne 2924
 
1.1%
royaume-uni 1620
 
0.6%
france,suisse 1066
 
0.4%
russie 805
 
0.3%
belgique 598
 
0.2%
australie 502
 
0.2%
Other values (557) 5588
 
2.2%

Most occurring characters

ValueCountFrequency (%)
s 362874
15.9%
t 338116
14.8%
a 247633
10.9%
n 245151
10.8%
i 184954
8.1%
- 170765
7.5%
U 170232
7.5%
É 168095
7.4%
e 97677
 
4.3%
r 66956
 
2.9%
Other values (110) 226271
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1671706
73.4%
Uppercase Letter 430706
 
18.9%
Dash Punctuation 170765
 
7.5%
Other Punctuation 5118
 
0.2%
Space Separator 255
 
< 0.1%
Other Letter 168
 
< 0.1%
Decimal Number 3
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%
Spacing Mark 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 18
 
10.7%
ل 16
 
9.5%
ة 13
 
7.7%
ع 11
 
6.5%
س 10
 
6.0%
د 8
 
4.8%
م 8
 
4.8%
ي 7
 
4.2%
ن 7
 
4.2%
و 7
 
4.2%
Other values (33) 63
37.5%
Lowercase Letter
ValueCountFrequency (%)
s 362874
21.7%
t 338116
20.2%
a 247633
14.8%
n 245151
14.7%
i 184954
11.1%
e 97677
 
5.8%
r 66956
 
4.0%
c 64938
 
3.9%
u 16859
 
1.0%
l 14114
 
0.8%
Other values (30) 32434
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
U 170232
39.5%
É 168095
39.0%
F 64627
 
15.0%
S 10455
 
2.4%
A 5948
 
1.4%
E 3247
 
0.8%
R 3166
 
0.7%
B 1742
 
0.4%
P 934
 
0.2%
I 569
 
0.1%
Other values (17) 1691
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 4985
97.4%
: 131
 
2.6%
' 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
7 2
66.7%
6 1
33.3%
Nonspacing Mark
ValueCountFrequency (%)
1
50.0%
1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 170765
100.0%
Space Separator
ValueCountFrequency (%)
255
100.0%
Spacing Mark
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2102403
92.3%
Common 176141
 
7.7%
Arabic 121
 
< 0.1%
Thai 38
 
< 0.1%
Cyrillic 9
 
< 0.1%
Han 8
 
< 0.1%
Devanagari 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 362874
17.3%
t 338116
16.1%
a 247633
11.8%
n 245151
11.7%
i 184954
8.8%
U 170232
8.1%
É 168095
8.0%
e 97677
 
4.6%
r 66956
 
3.2%
c 64938
 
3.1%
Other values (50) 155777
7.4%
Thai
ValueCountFrequency (%)
5
13.2%
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (10) 10
26.3%
Arabic
ValueCountFrequency (%)
ا 18
14.9%
ل 16
13.2%
ة 13
10.7%
ع 11
9.1%
س 10
8.3%
د 8
6.6%
م 8
6.6%
ي 7
 
5.8%
ن 7
 
5.8%
و 7
 
5.8%
Other values (8) 16
13.2%
Common
ValueCountFrequency (%)
- 170765
96.9%
, 4985
 
2.8%
255
 
0.1%
: 131
 
0.1%
7 2
 
< 0.1%
' 2
 
< 0.1%
6 1
 
< 0.1%
Cyrillic
ValueCountFrequency (%)
а 3
33.3%
т 1
 
11.1%
н 1
 
11.1%
х 1
 
11.1%
с 1
 
11.1%
з 1
 
11.1%
К 1
 
11.1%
Han
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
2
25.0%
Devanagari
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2109731
92.6%
None 168804
 
7.4%
Arabic 121
 
< 0.1%
Thai 38
 
< 0.1%
IPA Ext 9
 
< 0.1%
Cyrillic 9
 
< 0.1%
CJK 8
 
< 0.1%
Devanagari 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 362874
17.2%
t 338116
16.0%
a 247633
11.7%
n 245151
11.6%
i 184954
8.8%
- 170765
8.1%
U 170232
8.1%
e 97677
 
4.6%
r 66956
 
3.2%
c 64938
 
3.1%
Other values (47) 160435
7.6%
None
ValueCountFrequency (%)
É 168095
99.6%
é 464
 
0.3%
è 178
 
0.1%
ï 38
 
< 0.1%
ç 17
 
< 0.1%
ë 8
 
< 0.1%
ô 2
 
< 0.1%
ê 1
 
< 0.1%
Î 1
 
< 0.1%
Arabic
ValueCountFrequency (%)
ا 18
14.9%
ل 16
13.2%
ة 13
10.7%
ع 11
9.1%
س 10
8.3%
د 8
6.6%
م 8
6.6%
ي 7
 
5.8%
ن 7
 
5.8%
و 7
 
5.8%
Other values (8) 16
13.2%
IPA Ext
ValueCountFrequency (%)
ə 9
100.0%
Thai
ValueCountFrequency (%)
5
13.2%
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (10) 10
26.3%
Cyrillic
ValueCountFrequency (%)
а 3
33.3%
т 1
 
11.1%
н 1
 
11.1%
х 1
 
11.1%
с 1
 
11.1%
з 1
 
11.1%
К 1
 
11.1%
CJK
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
2
25.0%
Devanagari
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct184576
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Memory size3.9 MiB
2024-06-07T17:45:59.095212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length234
Median length161
Mean length26.842211
Min length1

Characters and Unicode

Total characters6844066
Distinct characters607
Distinct categories20 ?
Distinct scripts12 ?
Distinct blocks20 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique163912 ?
Unique (%)64.3%

Sample

1st rowBanana Chips Sweetened (Whole)
2nd rowPeanuts
3rd rowOrganic Salted Nut Mix
4th rowOrganic Polenta
5th rowBreadshop Honey Gone Nuts Granola
ValueCountFrequency (%)
24307
 
2.3%
de 20079
 
1.9%
chocolate 11169
 
1.1%
cheese 10274
 
1.0%
sauce 9975
 
1.0%
organic 9105
 
0.9%
with 8089
 
0.8%
mix 7066
 
0.7%
au 6628
 
0.6%
cream 5938
 
0.6%
Other values (43718) 936424
89.3%
2024-06-07T17:45:59.645744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
797591
 
11.7%
e 679702
 
9.9%
a 519009
 
7.6%
r 398308
 
5.8%
i 390348
 
5.7%
o 354039
 
5.2%
t 319760
 
4.7%
n 306049
 
4.5%
s 291079
 
4.3%
l 280256
 
4.1%
Other values (597) 2507925
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4948730
72.3%
Uppercase Letter 904442
 
13.2%
Space Separator 797635
 
11.7%
Other Punctuation 132464
 
1.9%
Decimal Number 35804
 
0.5%
Dash Punctuation 13463
 
0.2%
Close Punctuation 4678
 
0.1%
Open Punctuation 4676
 
0.1%
Math Symbol 1068
 
< 0.1%
Other Letter 467
 
< 0.1%
Other values (10) 639
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ל 11
 
2.4%
ו 9
 
1.9%
º 8
 
1.7%
י 8
 
1.7%
8
 
1.7%
7
 
1.5%
ק 7
 
1.5%
6
 
1.3%
ل 6
 
1.3%
6
 
1.3%
Other values (257) 391
83.7%
Lowercase Letter
ValueCountFrequency (%)
e 679702
13.7%
a 519009
10.5%
r 398308
 
8.0%
i 390348
 
7.9%
o 354039
 
7.2%
t 319760
 
6.5%
n 306049
 
6.2%
s 291079
 
5.9%
l 280256
 
5.7%
u 212856
 
4.3%
Other values (136) 1197324
24.2%
Uppercase Letter
ValueCountFrequency (%)
C 142429
15.7%
S 110619
12.2%
P 78402
 
8.7%
B 73567
 
8.1%
M 57888
 
6.4%
F 44496
 
4.9%
G 37689
 
4.2%
T 37267
 
4.1%
O 34702
 
3.8%
R 33710
 
3.7%
Other values (89) 253673
28.0%
Other Punctuation
ValueCountFrequency (%)
, 83639
63.1%
& 19841
 
15.0%
' 13559
 
10.2%
% 7182
 
5.4%
. 3243
 
2.4%
; 1856
 
1.4%
! 1197
 
0.9%
/ 724
 
0.5%
: 649
 
0.5%
" 170
 
0.1%
Other values (11) 404
 
0.3%
Nonspacing Mark
ValueCountFrequency (%)
7
18.9%
6
16.2%
4
10.8%
́ 4
10.8%
4
10.8%
4
10.8%
3
8.1%
1
 
2.7%
̀ 1
 
2.7%
̈ 1
 
2.7%
Other values (2) 2
 
5.4%
Decimal Number
ValueCountFrequency (%)
0 10916
30.5%
1 6689
18.7%
2 4939
13.8%
5 3045
 
8.5%
4 2631
 
7.3%
3 2631
 
7.3%
6 1640
 
4.6%
8 1504
 
4.2%
7 1147
 
3.2%
9 662
 
1.8%
Other Symbol
ValueCountFrequency (%)
® 90
46.4%
° 81
41.8%
6
 
3.1%
6
 
3.1%
5
 
2.6%
© 2
 
1.0%
2
 
1.0%
🅫 1
 
0.5%
1
 
0.5%
Math Symbol
ValueCountFrequency (%)
+ 1045
97.8%
| 8
 
0.7%
= 6
 
0.6%
~ 3
 
0.3%
> 2
 
0.2%
< 2
 
0.2%
1
 
0.1%
× 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 4628
99.0%
[ 37
 
0.8%
{ 7
 
0.1%
3
 
0.1%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
4
44.4%
2
22.2%
2
22.2%
1
 
11.1%
Space Separator
ValueCountFrequency (%)
797591
> 99.9%
  43
 
< 0.1%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 13451
99.9%
7
 
0.1%
5
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4636
99.1%
] 36
 
0.8%
} 6
 
0.1%
Initial Punctuation
ValueCountFrequency (%)
« 136
95.1%
6
 
4.2%
1
 
0.7%
Final Punctuation
ValueCountFrequency (%)
» 135
91.2%
10
 
6.8%
3
 
2.0%
Currency Symbol
ValueCountFrequency (%)
$ 45
78.9%
11
 
19.3%
¢ 1
 
1.8%
Modifier Symbol
ValueCountFrequency (%)
` 16
61.5%
´ 9
34.6%
¨ 1
 
3.8%
Control
ValueCountFrequency (%)
 9
52.9%
œ 6
35.3%
Œ 2
 
11.8%
Connector Punctuation
ValueCountFrequency (%)
_ 7
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5834074
85.2%
Common 990389
 
14.5%
Cyrillic 18855
 
0.3%
Greek 251
 
< 0.1%
Han 144
 
< 0.1%
Thai 115
 
< 0.1%
Hebrew 83
 
< 0.1%
Hiragana 55
 
< 0.1%
Katakana 36
 
< 0.1%
Hangul 34
 
< 0.1%
Other values (2) 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 679702
 
11.7%
a 519009
 
8.9%
r 398308
 
6.8%
i 390348
 
6.7%
o 354039
 
6.1%
t 319760
 
5.5%
n 306049
 
5.2%
s 291079
 
5.0%
l 280256
 
4.8%
u 212856
 
3.6%
Other values (132) 2082668
35.7%
Han
ValueCountFrequency (%)
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
2
 
1.4%
2
 
1.4%
2
 
1.4%
2
 
1.4%
2
 
1.4%
2
 
1.4%
Other values (101) 120
83.3%
Common
ValueCountFrequency (%)
797591
80.5%
, 83639
 
8.4%
& 19841
 
2.0%
' 13559
 
1.4%
- 13451
 
1.4%
0 10916
 
1.1%
% 7182
 
0.7%
1 6689
 
0.7%
2 4939
 
0.5%
) 4636
 
0.5%
Other values (72) 27946
 
2.8%
Cyrillic
ValueCountFrequency (%)
о 2058
 
10.9%
а 1768
 
9.4%
е 1344
 
7.1%
н 1319
 
7.0%
и 1131
 
6.0%
р 1090
 
5.8%
с 1037
 
5.5%
к 971
 
5.1%
л 912
 
4.8%
т 679
 
3.6%
Other values (51) 6546
34.7%
Greek
ValueCountFrequency (%)
α 23
 
9.2%
ο 21
 
8.4%
ι 18
 
7.2%
ρ 15
 
6.0%
λ 14
 
5.6%
κ 10
 
4.0%
τ 10
 
4.0%
ς 8
 
3.2%
ά 8
 
3.2%
μ 8
 
3.2%
Other values (33) 116
46.2%
Thai
ValueCountFrequency (%)
8
 
7.0%
7
 
6.1%
7
 
6.1%
6
 
5.2%
6
 
5.2%
6
 
5.2%
5
 
4.3%
5
 
4.3%
5
 
4.3%
5
 
4.3%
Other values (27) 55
47.8%
Hiragana
ValueCountFrequency (%)
5
 
9.1%
5
 
9.1%
3
 
5.5%
3
 
5.5%
3
 
5.5%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
Other values (22) 25
45.5%
Hangul
ValueCountFrequency (%)
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (21) 21
61.8%
Katakana
ValueCountFrequency (%)
3
 
8.3%
2
 
5.6%
2
 
5.6%
2
 
5.6%
2
 
5.6%
1
 
2.8%
1
 
2.8%
1
 
2.8%
1
 
2.8%
1
 
2.8%
Other values (20) 20
55.6%
Hebrew
ValueCountFrequency (%)
ל 11
13.3%
ו 9
 
10.8%
י 8
 
9.6%
ק 7
 
8.4%
פ 6
 
7.2%
א 5
 
6.0%
ר 4
 
4.8%
ה 3
 
3.6%
ב 3
 
3.6%
מ 3
 
3.6%
Other values (13) 24
28.9%
Arabic
ValueCountFrequency (%)
ل 6
30.0%
ي 3
15.0%
م 2
 
10.0%
س 2
 
10.0%
ب 2
 
10.0%
ح 1
 
5.0%
ق 1
 
5.0%
ا 1
 
5.0%
د 1
 
5.0%
خ 1
 
5.0%
Inherited
ValueCountFrequency (%)
́ 4
40.0%
3
30.0%
1
 
10.0%
̀ 1
 
10.0%
̈ 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6772640
99.0%
None 51984
 
0.8%
Cyrillic 18855
 
0.3%
CJK 144
 
< 0.1%
Thai 115
 
< 0.1%
Hebrew 83
 
< 0.1%
Punctuation 55
 
< 0.1%
Hiragana 55
 
< 0.1%
Katakana 35
 
< 0.1%
Hangul 34
 
< 0.1%
Other values (10) 66
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
797591
 
11.8%
e 679702
 
10.0%
a 519009
 
7.7%
r 398308
 
5.9%
i 390348
 
5.8%
o 354039
 
5.2%
t 319760
 
4.7%
n 306049
 
4.5%
s 291079
 
4.3%
l 280256
 
4.1%
Other values (84) 2436499
36.0%
None
ValueCountFrequency (%)
é 28969
55.7%
à 4999
 
9.6%
è 4378
 
8.4%
â 2386
 
4.6%
ê 1434
 
2.8%
û 1248
 
2.4%
ü 964
 
1.9%
ä 699
 
1.3%
ç 680
 
1.3%
ô 672
 
1.3%
Other values (145) 5555
 
10.7%
Cyrillic
ValueCountFrequency (%)
о 2058
 
10.9%
а 1768
 
9.4%
е 1344
 
7.1%
н 1319
 
7.0%
и 1131
 
6.0%
р 1090
 
5.8%
с 1037
 
5.5%
к 971
 
5.1%
л 912
 
4.8%
т 679
 
3.6%
Other values (51) 6546
34.7%
Currency Symbols
ValueCountFrequency (%)
11
100.0%
Hebrew
ValueCountFrequency (%)
ל 11
13.3%
ו 9
 
10.8%
י 8
 
9.6%
ק 7
 
8.4%
פ 6
 
7.2%
א 5
 
6.0%
ר 4
 
4.8%
ה 3
 
3.6%
ב 3
 
3.6%
מ 3
 
3.6%
Other values (13) 24
28.9%
Punctuation
ValueCountFrequency (%)
10
18.2%
9
16.4%
9
16.4%
7
12.7%
6
10.9%
5
9.1%
3
 
5.5%
3
 
5.5%
1
 
1.8%
1
 
1.8%
Thai
ValueCountFrequency (%)
8
 
7.0%
7
 
6.1%
7
 
6.1%
6
 
5.2%
6
 
5.2%
6
 
5.2%
5
 
4.3%
5
 
4.3%
5
 
4.3%
5
 
4.3%
Other values (27) 55
47.8%
Misc Symbols
ValueCountFrequency (%)
6
100.0%
Letterlike Symbols
ValueCountFrequency (%)
6
46.2%
5
38.5%
2
 
15.4%
Arabic
ValueCountFrequency (%)
ل 6
30.0%
ي 3
15.0%
م 2
 
10.0%
س 2
 
10.0%
ب 2
 
10.0%
ح 1
 
5.0%
ق 1
 
5.0%
ا 1
 
5.0%
د 1
 
5.0%
خ 1
 
5.0%
Hiragana
ValueCountFrequency (%)
5
 
9.1%
5
 
9.1%
3
 
5.5%
3
 
5.5%
3
 
5.5%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
Other values (22) 25
45.5%
Diacriticals
ValueCountFrequency (%)
́ 4
66.7%
̀ 1
 
16.7%
̈ 1
 
16.7%
Katakana
ValueCountFrequency (%)
4
 
11.4%
3
 
8.6%
2
 
5.7%
2
 
5.7%
2
 
5.7%
2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (16) 16
45.7%
CJK
ValueCountFrequency (%)
3
 
2.1%
3
 
2.1%
3
 
2.1%
3
 
2.1%
2
 
1.4%
2
 
1.4%
2
 
1.4%
2
 
1.4%
2
 
1.4%
2
 
1.4%
Other values (101) 120
83.3%
VS
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Hangul
ValueCountFrequency (%)
2
 
5.9%
2
 
5.9%
2
 
5.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (21) 21
61.8%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🅫 1
100.0%
Latin Ext Additional
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Math Operators
ValueCountFrequency (%)
1
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%

brands
Categorical

HIGH CARDINALITY  MISSING 

Distinct45792
Distinct (%)18.2%
Missing3280
Missing (%)1.3%
Memory size5.4 MiB
Carrefour
 
2504
Meijer
 
1945
Auchan
 
1939
U
 
1754
Kroger
 
1632
Other values (45787)
241920 

Length

Max length228
Median length155
Mean length15.766129
Min length1

Characters and Unicode

Total characters3968240
Distinct characters393
Distinct categories16 ?
Distinct scripts11 ?
Distinct blocks13 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25210 ?
Unique (%)10.0%

Sample

1st rowTorn & Glasser
2nd rowGrizzlies
3rd rowBob's Red Mill
4th rowUnfi
5th rowLundberg

Common Values

ValueCountFrequency (%)
Carrefour 2504
 
1.0%
Meijer 1945
 
0.8%
Auchan 1939
 
0.8%
U 1754
 
0.7%
Kroger 1632
 
0.6%
Leader Price 1412
 
0.6%
Spartan 1323
 
0.5%
Ahold 1320
 
0.5%
Casino 1293
 
0.5%
Roundy's 1252
 
0.5%
Other values (45782) 235320
92.3%
(Missing) 3280
 
1.3%

Length

2024-06-07T17:45:59.821269image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 34189
 
5.8%
foods 14040
 
2.4%
llc 8694
 
1.5%
company 8525
 
1.4%
7636
 
1.3%
co 7366
 
1.2%
food 6890
 
1.2%
the 4678
 
0.8%
market 4023
 
0.7%
stores 3895
 
0.7%
Other values (30953) 494067
83.2%

Most occurring characters

ValueCountFrequency (%)
404219
 
10.2%
e 330918
 
8.3%
a 286274
 
7.2%
r 259350
 
6.5%
o 256665
 
6.5%
n 220687
 
5.6%
i 199614
 
5.0%
s 175776
 
4.4%
t 152876
 
3.9%
l 146620
 
3.7%
Other values (383) 1535241
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2774702
69.9%
Uppercase Letter 615265
 
15.5%
Space Separator 404219
 
10.2%
Other Punctuation 155211
 
3.9%
Dash Punctuation 9795
 
0.2%
Decimal Number 6085
 
0.2%
Open Punctuation 1118
 
< 0.1%
Close Punctuation 1117
 
< 0.1%
Other Letter 282
 
< 0.1%
Math Symbol 235
 
< 0.1%
Other values (6) 211
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
7.8%
21
 
7.4%
º 11
 
3.9%
11
 
3.9%
11
 
3.9%
11
 
3.9%
ا 7
 
2.5%
ك 6
 
2.1%
6
 
2.1%
5
 
1.8%
Other values (121) 171
60.6%
Lowercase Letter
ValueCountFrequency (%)
e 330918
11.9%
a 286274
10.3%
r 259350
9.3%
o 256665
9.3%
n 220687
 
8.0%
i 199614
 
7.2%
s 175776
 
6.3%
t 152876
 
5.5%
l 146620
 
5.3%
c 120363
 
4.3%
Other values (105) 625559
22.5%
Uppercase Letter
ValueCountFrequency (%)
C 66028
 
10.7%
S 53148
 
8.6%
F 46802
 
7.6%
I 44914
 
7.3%
M 43380
 
7.1%
B 39192
 
6.4%
L 36374
 
5.9%
P 30741
 
5.0%
A 26881
 
4.4%
T 26569
 
4.3%
Other values (81) 201236
32.7%
Other Punctuation
ValueCountFrequency (%)
. 54488
35.1%
, 51423
33.1%
' 27090
17.5%
/ 10402
 
6.7%
& 7961
 
5.1%
: 2540
 
1.6%
! 1039
 
0.7%
" 150
 
0.1%
· 26
 
< 0.1%
@ 22
 
< 0.1%
Other values (9) 70
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
3 1212
19.9%
5 1130
18.6%
6 1018
16.7%
2 559
9.2%
1 540
8.9%
0 477
 
7.8%
7 442
 
7.3%
4 276
 
4.5%
9 236
 
3.9%
8 195
 
3.2%
Nonspacing Mark
ValueCountFrequency (%)
12
41.4%
6
20.7%
4
 
13.8%
3
 
10.3%
2
 
6.9%
1
 
3.4%
1
 
3.4%
Math Symbol
ValueCountFrequency (%)
+ 232
98.7%
| 2
 
0.9%
~ 1
 
0.4%
Other Symbol
ValueCountFrequency (%)
® 19
79.2%
4
 
16.7%
° 1
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 9789
99.9%
6
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1108
99.1%
[ 10
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 1107
99.1%
] 10
 
0.9%
Currency Symbol
ValueCountFrequency (%)
$ 71
89.9%
8
 
10.1%
Final Punctuation
ValueCountFrequency (%)
» 31
83.8%
6
 
16.2%
Modifier Symbol
ValueCountFrequency (%)
´ 7
77.8%
` 2
 
22.2%
Space Separator
ValueCountFrequency (%)
404219
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3382248
85.2%
Common 577962
 
14.6%
Cyrillic 7681
 
0.2%
Thai 161
 
< 0.1%
Han 70
 
< 0.1%
Greek 49
 
< 0.1%
Arabic 27
 
< 0.1%
Hebrew 19
 
< 0.1%
Hangul 14
 
< 0.1%
Katakana 8
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 330918
 
9.8%
a 286274
 
8.5%
r 259350
 
7.7%
o 256665
 
7.6%
n 220687
 
6.5%
i 199614
 
5.9%
s 175776
 
5.2%
t 152876
 
4.5%
l 146620
 
4.3%
c 120363
 
3.6%
Other values (107) 1233105
36.5%
Cyrillic
ValueCountFrequency (%)
о 672
 
8.7%
а 659
 
8.6%
е 597
 
7.8%
р 501
 
6.5%
н 480
 
6.2%
и 443
 
5.8%
к 398
 
5.2%
с 358
 
4.7%
л 268
 
3.5%
т 261
 
3.4%
Other values (52) 3044
39.6%
Han
ValueCountFrequency (%)
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (50) 50
71.4%
Common
ValueCountFrequency (%)
404219
69.9%
. 54488
 
9.4%
, 51423
 
8.9%
' 27090
 
4.7%
/ 10402
 
1.8%
- 9789
 
1.7%
& 7961
 
1.4%
: 2540
 
0.4%
3 1212
 
0.2%
5 1130
 
0.2%
Other values (39) 7708
 
1.3%
Thai
ValueCountFrequency (%)
22
13.7%
21
13.0%
12
 
7.5%
11
 
6.8%
11
 
6.8%
11
 
6.8%
6
 
3.7%
6
 
3.7%
5
 
3.1%
5
 
3.1%
Other values (24) 51
31.7%
Greek
ValueCountFrequency (%)
ν 8
16.3%
ω 4
 
8.2%
η 4
 
8.2%
Κ 2
 
4.1%
τ 2
 
4.1%
Ι 2
 
4.1%
ί 2
 
4.1%
Ν 2
 
4.1%
Ο 2
 
4.1%
Υ 2
 
4.1%
Other values (18) 19
38.8%
Hebrew
ValueCountFrequency (%)
ק 3
15.8%
ו 3
15.8%
ה 2
10.5%
פ 2
10.5%
מ 1
 
5.3%
ס 1
 
5.3%
י 1
 
5.3%
צ 1
 
5.3%
ת 1
 
5.3%
ל 1
 
5.3%
Other values (3) 3
15.8%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Other values (2) 2
14.3%
Arabic
ValueCountFrequency (%)
ا 7
25.9%
ك 6
22.2%
و 5
18.5%
ل 3
11.1%
ن 2
 
7.4%
د 1
 
3.7%
ي 1
 
3.7%
ف 1
 
3.7%
ص 1
 
3.7%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Hiragana
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3945763
99.4%
None 14466
 
0.4%
Cyrillic 7681
 
0.2%
Thai 161
 
< 0.1%
CJK 70
 
< 0.1%
Arabic 28
 
< 0.1%
Hebrew 19
 
< 0.1%
Punctuation 17
 
< 0.1%
Hangul 14
 
< 0.1%
Currency Symbols 8
 
< 0.1%
Other values (3) 13
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
404219
 
10.2%
e 330918
 
8.4%
a 286274
 
7.3%
r 259350
 
6.6%
o 256665
 
6.5%
n 220687
 
5.6%
i 199614
 
5.1%
s 175776
 
4.5%
t 152876
 
3.9%
l 146620
 
3.7%
Other values (77) 1512764
38.3%
None
ValueCountFrequency (%)
é 8152
56.4%
è 2782
 
19.2%
ü 545
 
3.8%
ó 439
 
3.0%
ô 325
 
2.2%
í 240
 
1.7%
â 224
 
1.5%
ä 212
 
1.5%
ê 188
 
1.3%
î 157
 
1.1%
Other values (90) 1202
 
8.3%
Cyrillic
ValueCountFrequency (%)
о 672
 
8.7%
а 659
 
8.6%
е 597
 
7.8%
р 501
 
6.5%
н 480
 
6.2%
и 443
 
5.8%
к 398
 
5.2%
с 358
 
4.7%
л 268
 
3.5%
т 261
 
3.4%
Other values (52) 3044
39.6%
Thai
ValueCountFrequency (%)
22
13.7%
21
13.0%
12
 
7.5%
11
 
6.8%
11
 
6.8%
11
 
6.8%
6
 
3.7%
6
 
3.7%
5
 
3.1%
5
 
3.1%
Other values (24) 51
31.7%
Currency Symbols
ValueCountFrequency (%)
8
100.0%
Arabic
ValueCountFrequency (%)
ا 7
25.0%
ك 6
21.4%
و 5
17.9%
ل 3
10.7%
ن 2
 
7.1%
د 1
 
3.6%
، 1
 
3.6%
ي 1
 
3.6%
ف 1
 
3.6%
ص 1
 
3.6%
Punctuation
ValueCountFrequency (%)
6
35.3%
6
35.3%
4
23.5%
1
 
5.9%
Letterlike Symbols
ValueCountFrequency (%)
4
100.0%
Hebrew
ValueCountFrequency (%)
ק 3
15.8%
ו 3
15.8%
ה 2
10.5%
פ 2
10.5%
מ 1
 
5.3%
ס 1
 
5.3%
י 1
 
5.3%
צ 1
 
5.3%
ת 1
 
5.3%
ל 1
 
5.3%
Other values (3) 3
15.8%
CJK
ValueCountFrequency (%)
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
2
 
2.9%
Other values (50) 50
71.4%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Other values (2) 2
14.3%
Hiragana
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

additives_n
Real number (ℝ)

ZEROS 

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7051856
Minimum-1
Maximum31
Zeros82104
Zeros (%)32.2%
Negative25244
Negative (%)9.9%
Memory size3.9 MiB
2024-06-07T17:45:59.960896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median1
Q33
95-th percentile7
Maximum31
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5527784
Coefficient of variation (CV)1.4970677
Kurtosis6.8785431
Mean1.7051856
Median Absolute Deviation (MAD)1
Skewness2.0516666
Sum434778
Variance6.5166775
MonotonicityNot monotonic
2024-06-07T17:46:00.092544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 82104
32.2%
1 44028
17.3%
2 35091
13.8%
-1 25244
 
9.9%
3 22854
 
9.0%
4 14640
 
5.7%
5 10392
 
4.1%
6 6876
 
2.7%
7 4395
 
1.7%
8 3171
 
1.2%
Other values (22) 6179
 
2.4%
ValueCountFrequency (%)
-1 25244
 
9.9%
0 82104
32.2%
1 44028
17.3%
2 35091
13.8%
3 22854
 
9.0%
4 14640
 
5.7%
5 10392
 
4.1%
6 6876
 
2.7%
7 4395
 
1.7%
8 3171
 
1.2%
ValueCountFrequency (%)
31 4
 
< 0.1%
29 2
 
< 0.1%
28 2
 
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 11
< 0.1%
24 10
 
< 0.1%
23 14
< 0.1%
22 26
< 0.1%
21 20
< 0.1%

energy_100g
Real number (ℝ)

ZEROS 

Distinct3817
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131.0096
Minimum0
Maximum3700
Zeros6091
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:00.230176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84
Q1393
median1117
Q31674
95-th percentile2389
Maximum3700
Range3700
Interquartile range (IQR)1281

Descriptive statistics

Standard deviation782.9411
Coefficient of variation (CV)0.69224972
Kurtosis-0.50182767
Mean1131.0096
Median Absolute Deviation (MAD)657
Skewness0.40962332
Sum2.8837805 × 108
Variance612996.77
MonotonicityNot monotonic
2024-06-07T17:46:00.632125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6091
 
2.4%
2092 5074
 
2.0%
1674 4010
 
1.6%
1494 3912
 
1.5%
1644 3276
 
1.3%
1393 3219
 
1.3%
1046 2943
 
1.2%
1569 2824
 
1.1%
1795 2347
 
0.9%
1197 2312
 
0.9%
Other values (3807) 218966
85.9%
ValueCountFrequency (%)
0 6091
2.4%
0.02 1
 
< 0.1%
0.42 1
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 7
 
< 0.1%
0.9 4
 
< 0.1%
0.92 4
 
< 0.1%
1 49
 
< 0.1%
1.1 1
 
< 0.1%
ValueCountFrequency (%)
3700 256
0.1%
3699 5
 
< 0.1%
3697 1
 
< 0.1%
3696 3
 
< 0.1%
3693 6
 
< 0.1%
3692 1
 
< 0.1%
3691 1
 
< 0.1%
3690 3
 
< 0.1%
3689 2
 
< 0.1%
3686 1
 
< 0.1%

salt_100g
Real number (ℝ)

ZEROS 

Distinct5500
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5783321
Minimum0
Maximum100
Zeros36339
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:00.772724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.05334
median0.56388
Q31.36144
95-th percentile4
Maximum100
Range100
Interquartile range (IQR)1.3081

Descriptive statistics

Standard deviation6.2312293
Coefficient of variation (CV)3.9479835
Kurtosis141.59793
Mean1.5783321
Median Absolute Deviation (MAD)0.54388
Skewness11.077406
Sum402433.66
Variance38.828219
MonotonicityNot monotonic
2024-06-07T17:46:00.907392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36339
 
14.3%
0.01 3564
 
1.4%
0.1 3305
 
1.3%
1 2153
 
0.8%
0.0254 2091
 
0.8%
1.27 1938
 
0.8%
1.63322 1824
 
0.7%
0.127 1775
 
0.7%
0.03 1558
 
0.6%
0.02032 1537
 
0.6%
Other values (5490) 198890
78.0%
ValueCountFrequency (%)
0 36339
14.3%
5 × 10-81
 
< 0.1%
9.999999 × 10-82
 
< 0.1%
1 × 10-61
 
< 0.1%
5 × 10-61
 
< 0.1%
7.874 × 10-61
 
< 0.1%
1 × 10-55
 
< 0.1%
1.3 × 10-54
 
< 0.1%
2 × 10-51
 
< 0.1%
2.413 × 10-51
 
< 0.1%
ValueCountFrequency (%)
100 21
 
< 0.1%
99.93 1
 
< 0.1%
99.90582 111
< 0.1%
99.9 8
 
< 0.1%
99.822 5
 
< 0.1%
99.8 3
 
< 0.1%
99.78644 10
 
< 0.1%
99.64674 3
 
< 0.1%
99.568 1
 
< 0.1%
99.5 1
 
< 0.1%

sodium_100g
Real number (ℝ)

ZEROS 

Distinct5251
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64268795
Minimum0
Maximum100
Zeros36343
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:01.043031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.021
median0.222
Q30.536
95-th percentile1.583
Maximum100
Range100
Interquartile range (IQR)0.515

Descriptive statistics

Standard deviation2.6506021
Coefficient of variation (CV)4.1242443
Kurtosis162.08847
Mean0.64268795
Median Absolute Deviation (MAD)0.21412598
Skewness11.525525
Sum163868.72
Variance7.0256914
MonotonicityNot monotonic
2024-06-07T17:46:01.179664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36343
 
14.3%
0.003937007874 3559
 
1.4%
0.03937007874 3290
 
1.3%
0.3937007874 2138
 
0.8%
0.01 2090
 
0.8%
0.5 1936
 
0.8%
0.643 1847
 
0.7%
0.01181102362 1842
 
0.7%
0.05 1775
 
0.7%
0.008 1541
 
0.6%
Other values (5241) 198613
77.9%
ValueCountFrequency (%)
0 36343
14.3%
1.968503937 × 10-81
 
< 0.1%
3.93700748 × 10-82
 
< 0.1%
3.937007874 × 10-71
 
< 0.1%
1.968503937 × 10-61
 
< 0.1%
3.1 × 10-61
 
< 0.1%
3.937007874 × 10-65
 
< 0.1%
5.118110236 × 10-64
 
< 0.1%
7.874015748 × 10-61
 
< 0.1%
9.5 × 10-61
 
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
92.5 1
< 0.1%
83 1
< 0.1%
75 2
< 0.1%
74 1
< 0.1%
71.429 1
< 0.1%
70 1
< 0.1%
62.5 1
< 0.1%
60.3 1
< 0.1%
59 1
< 0.1%

fiber_100g
Real number (ℝ)

ZEROS 

Distinct1010
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2069436
Minimum0
Maximum100
Zeros124820
Zeros (%)49.0%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:01.313307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.3
Q33
95-th percentile9.6
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.219424
Coefficient of variation (CV)1.9118857
Kurtosis58.132747
Mean2.2069436
Median Absolute Deviation (MAD)0.3
Skewness5.4282559
Sum562713.25
Variance17.803539
MonotonicityNot monotonic
2024-06-07T17:46:01.454928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124820
49.0%
3.6 8511
 
3.3%
3.3 3986
 
1.6%
1.8 3874
 
1.5%
0.8 3799
 
1.5%
7.1 3703
 
1.5%
1.6 3413
 
1.3%
2 3399
 
1.3%
1.2 3259
 
1.3%
2.4 3221
 
1.3%
Other values (1000) 92989
36.5%
ValueCountFrequency (%)
0 124820
49.0%
0.0001 2
 
< 0.1%
0.0002 1
 
< 0.1%
0.001 16
 
< 0.1%
0.002 3
 
< 0.1%
0.004 1
 
< 0.1%
0.00416 1
 
< 0.1%
0.005 2
 
< 0.1%
0.01 72
 
< 0.1%
0.016 1
 
< 0.1%
ValueCountFrequency (%)
100 10
< 0.1%
99 1
 
< 0.1%
94.8 1
 
< 0.1%
92.4 1
 
< 0.1%
90 1
 
< 0.1%
88 2
 
< 0.1%
87.5 1
 
< 0.1%
87 1
 
< 0.1%
86.2 1
 
< 0.1%
85.2 1
 
< 0.1%

sugars_100g
Real number (ℝ)

ZEROS 

Distinct4052
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.10314
Minimum0
Maximum100
Zeros50158
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:01.595551image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.8
median4.8
Q322.22
95-th percentile60.774
Maximum100
Range100
Interquartile range (IQR)21.42

Descriptive statistics

Standard deviation20.84872
Coefficient of variation (CV)1.3804228
Kurtosis2.4900988
Mean15.10314
Median Absolute Deviation (MAD)4.8
Skewness1.7375937
Sum3850908
Variance434.66911
MonotonicityNot monotonic
2024-06-07T17:46:01.728197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50158
 
19.7%
3.57 7142
 
2.8%
0.5 4359
 
1.7%
3.33 3700
 
1.5%
1 2567
 
1.0%
20 2323
 
0.9%
6.67 2264
 
0.9%
10 2168
 
0.9%
50 2101
 
0.8%
7.14 2024
 
0.8%
Other values (4042) 176168
69.1%
ValueCountFrequency (%)
0 50158
19.7%
0.0001 8
 
< 0.1%
0.0005 1
 
< 0.1%
0.001 24
 
< 0.1%
0.0019 2
 
< 0.1%
0.0048 1
 
< 0.1%
0.007 1
 
< 0.1%
0.01 85
 
< 0.1%
0.0108 1
 
< 0.1%
0.02 27
 
< 0.1%
ValueCountFrequency (%)
100 921
0.4%
99.95 1
 
< 0.1%
99.9 6
 
< 0.1%
99.8 3
 
< 0.1%
99.7 10
 
< 0.1%
99.6 4
 
< 0.1%
99.5 10
 
< 0.1%
99.3 2
 
< 0.1%
99.2 3
 
< 0.1%
99 40
 
< 0.1%

fat_100g
Real number (ℝ)

ZEROS 

Distinct3370
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.979729
Minimum0
Maximum100
Zeros78545
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:01.859845image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.79
Q319
95-th percentile45
Maximum100
Range100
Interquartile range (IQR)19

Descriptive statistics

Standard deviation17.247552
Coefficient of variation (CV)1.439728
Kurtosis6.5670472
Mean11.979729
Median Absolute Deviation (MAD)3.79
Skewness2.2702762
Sum3054519.5
Variance297.47804
MonotonicityNot monotonic
2024-06-07T17:46:01.995483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78545
30.8%
25 3375
 
1.3%
32.14 2978
 
1.2%
0.5 2967
 
1.2%
20 2656
 
1.0%
1.79 2526
 
1.0%
28.57 2459
 
1.0%
21.43 2411
 
0.9%
0.1 2367
 
0.9%
10 2238
 
0.9%
Other values (3360) 152452
59.8%
ValueCountFrequency (%)
0 78545
30.8%
0.0001 2
 
< 0.1%
0.000133 1
 
< 0.1%
0.001 1
 
< 0.1%
0.003 1
 
< 0.1%
0.004 2
 
< 0.1%
0.005 3
 
< 0.1%
0.007 1
 
< 0.1%
0.01 42
 
< 0.1%
0.012 2
 
< 0.1%
ValueCountFrequency (%)
100 1280
0.5%
99.9 16
 
< 0.1%
99.85 1
 
< 0.1%
99.82 1
 
< 0.1%
99.8 17
 
< 0.1%
99.7 5
 
< 0.1%
99.4 5
 
< 0.1%
99 5
 
< 0.1%
98.73 1
 
< 0.1%
98.5 1
 
< 0.1%

saturated_fat_100g
Real number (ℝ)

ZEROS 

Distinct2192
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5437063
Minimum0
Maximum100
Zeros96736
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:02.132113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36.58
95-th percentile19
Maximum100
Range100
Interquartile range (IQR)6.58

Descriptive statistics

Standard deviation7.6251009
Coefficient of variation (CV)1.6781677
Kurtosis23.985088
Mean4.5437063
Median Absolute Deviation (MAD)1
Skewness3.6365845
Sum1158527
Variance58.142164
MonotonicityNot monotonic
2024-06-07T17:46:02.271742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 96736
37.9%
0.1 5227
 
2.1%
3.57 3480
 
1.4%
0.5 3079
 
1.2%
7.14 2875
 
1.1%
0.2 2558
 
1.0%
1 2335
 
0.9%
0.3 2302
 
0.9%
3.33 2211
 
0.9%
1.79 2189
 
0.9%
Other values (2182) 131982
51.8%
ValueCountFrequency (%)
0 96736
37.9%
0.0001 11
 
< 0.1%
0.001 30
 
< 0.1%
0.002 10
 
< 0.1%
0.003 4
 
< 0.1%
0.0032 1
 
< 0.1%
0.004 3
 
< 0.1%
0.005 11
 
< 0.1%
0.006 2
 
< 0.1%
0.00667 1
 
< 0.1%
ValueCountFrequency (%)
100 12
< 0.1%
99.9 1
 
< 0.1%
99 2
 
< 0.1%
98 1
 
< 0.1%
96 2
 
< 0.1%
95.5 1
 
< 0.1%
95 5
< 0.1%
94 2
 
< 0.1%
93.8 1
 
< 0.1%
93.33 3
 
< 0.1%

cholesterol_100g
Real number (ℝ)

SKEWED  ZEROS 

Distinct535
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011335686
Minimum0
Maximum95.238
Zeros200361
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size3.9 MiB
2024-06-07T17:46:02.403388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.071
Maximum95.238
Range95.238
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26935215
Coefficient of variation (CV)23.761433
Kurtosis91156.825
Mean0.011335686
Median Absolute Deviation (MAD)0
Skewness293.63875
Sum2890.3053
Variance0.07255058
MonotonicityNot monotonic
2024-06-07T17:46:02.535011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 200361
78.6%
0.071 2461
 
1.0%
0.107 2235
 
0.9%
0.012 1908
 
0.7%
0.089 1662
 
0.7%
0.054 1651
 
0.6%
0.018 1587
 
0.6%
0.004 1503
 
0.6%
0.036 1385
 
0.5%
0.008 1209
 
0.5%
Other values (525) 39012
 
15.3%
ValueCountFrequency (%)
0 200361
78.6%
4.5 × 10-51
 
< 0.1%
7.1 × 10-51
 
< 0.1%
0.0001 5
 
< 0.1%
0.0002 5
 
< 0.1%
0.0004 1
 
< 0.1%
0.000416 1
 
< 0.1%
0.00046 1
 
< 0.1%
0.0005 2
 
< 0.1%
0.0008 1
 
< 0.1%
ValueCountFrequency (%)
95.238 1
< 0.1%
70.588 1
< 0.1%
62.5 1
< 0.1%
13.846 1
< 0.1%
10.9 1
< 0.1%
1.58 1
< 0.1%
1.291 1
< 0.1%
1.25 1
< 0.1%
1.081 1
< 0.1%
0.996 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
a
72728 
d
62019 
c
44924 
e
42377 
b
32926 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters254974
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowb
3rd rowd
4th rowa
5th rowa

Common Values

ValueCountFrequency (%)
a 72728
28.5%
d 62019
24.3%
c 44924
17.6%
e 42377
16.6%
b 32926
12.9%

Length

2024-06-07T17:46:02.662669image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T17:46:02.765424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
a 72728
28.5%
d 62019
24.3%
c 44924
17.6%
e 42377
16.6%
b 32926
12.9%

Most occurring characters

ValueCountFrequency (%)
a 72728
28.5%
d 62019
24.3%
c 44924
17.6%
e 42377
16.6%
b 32926
12.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 254974
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 72728
28.5%
d 62019
24.3%
c 44924
17.6%
e 42377
16.6%
b 32926
12.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 254974
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 72728
28.5%
d 62019
24.3%
c 44924
17.6%
e 42377
16.6%
b 32926
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 254974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 72728
28.5%
d 62019
24.3%
c 44924
17.6%
e 42377
16.6%
b 32926
12.9%

nutrition_score_uk_100g
Real number (ℝ)

ZEROS 

Distinct22209
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0157221
Minimum-15
Maximum40
Zeros12521
Zeros (%)4.9%
Negative37141
Negative (%)14.6%
Memory size3.9 MiB
2024-06-07T17:46:02.888093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-4
Q12
median9
Q316
95-th percentile24
Maximum40
Range55
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.7620999
Coefficient of variation (CV)0.97186889
Kurtosis-0.86589005
Mean9.0157221
Median Absolute Deviation (MAD)7
Skewness0.19504308
Sum2298774.7
Variance76.774394
MonotonicityNot monotonic
2024-06-07T17:46:03.016750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12521
 
4.9%
1 11784
 
4.6%
2 10932
 
4.3%
14 10565
 
4.1%
-1 8730
 
3.4%
13 8309
 
3.3%
12 8140
 
3.2%
11 7980
 
3.1%
3 7489
 
2.9%
20 7301
 
2.9%
Other values (22199) 161223
63.2%
ValueCountFrequency (%)
-15 12
 
< 0.1%
-14 5
 
< 0.1%
-13 23
 
< 0.1%
-12.98946843 1
 
< 0.1%
-12 46
< 0.1%
-11.89017976 1
 
< 0.1%
-11.71202015 1
 
< 0.1%
-11.11047737 1
 
< 0.1%
-11.02351469 1
 
< 0.1%
-11 90
< 0.1%
ValueCountFrequency (%)
40 33
< 0.1%
39.62928274 27
< 0.1%
39.57922926 14
< 0.1%
38.90178138 7
 
< 0.1%
38.74275539 1
 
< 0.1%
38 1
 
< 0.1%
37.86717433 3
 
< 0.1%
37.22136799 1
 
< 0.1%
37.13967297 9
 
< 0.1%
37 2
 
< 0.1%

nutrition_score_fr_100g
Real number (ℝ)

ZEROS 

Distinct22224
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1377468
Minimum-15
Maximum40
Zeros11704
Zeros (%)4.6%
Negative35481
Negative (%)13.9%
Memory size3.9 MiB
2024-06-07T17:46:03.147402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-4
Q12
median9
Q315.613139
95-th percentile24
Maximum40
Range55
Interquartile range (IQR)13.613139

Descriptive statistics

Standard deviation8.6223733
Coefficient of variation (CV)0.9435995
Kurtosis-0.81166515
Mean9.1377468
Median Absolute Deviation (MAD)7
Skewness0.16934547
Sum2329887.9
Variance74.345322
MonotonicityNot monotonic
2024-06-07T17:46:03.277053image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11704
 
4.6%
1 11130
 
4.4%
14 11124
 
4.4%
2 10457
 
4.1%
13 8725
 
3.4%
-1 8707
 
3.4%
12 8558
 
3.4%
11 8537
 
3.3%
3 7725
 
3.0%
15 7457
 
2.9%
Other values (22214) 160850
63.1%
ValueCountFrequency (%)
-15 1
 
< 0.1%
-14.56626981 1
 
< 0.1%
-14.53003013 1
 
< 0.1%
-14.43958461 1
 
< 0.1%
-14.42682726 1
 
< 0.1%
-14.42473158 2
 
< 0.1%
-14.38149172 2
 
< 0.1%
-14.34924791 3
 
< 0.1%
-14 5
 
< 0.1%
-13 23
< 0.1%
ValueCountFrequency (%)
40 4
 
< 0.1%
38.5578851 1
 
< 0.1%
38.51842973 1
 
< 0.1%
38.49152462 3
 
< 0.1%
38.4492283 1
 
< 0.1%
38.44860451 1
 
< 0.1%
38.44609177 20
< 0.1%
38.43481639 2
 
< 0.1%
38.41384923 1
 
< 0.1%
38.09412942 27
< 0.1%

Interactions

2024-06-07T17:45:55.485071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.181329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.535706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.900057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.219547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.546979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.845504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.205873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:50.911330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.564888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.062873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:55.628692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.326938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.655396image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.020734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.348184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.672647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.975157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.320558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.083843image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.683563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.182580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:55.762330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.467564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.776069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.140415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.481838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.802294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.093841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.499091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.212533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.823189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.318189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:55.882017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.603218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.892759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.256107image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.602506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.919008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.204545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.612777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.396007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.986753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.449839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.029614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.722889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.009446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.366817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.718194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.027698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.319245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.735459image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.528652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.100455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.585476image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.185229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.836587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.127125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.477521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.831889image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.140395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.428943image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.886045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.698208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.227109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.707158image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.309890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:41.951270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.248799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.591223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.948580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.254086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.560589image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:50.277026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.825863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.374722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.825841image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.433532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.067965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.375466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.700920image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.073244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.363802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-06-07T17:45:50.387706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:51.981442image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.517332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:54.962468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.554218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.182660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.508116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.836552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.190929image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.476490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.810921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:50.498408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.152981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.658965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:55.084145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.681872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.298341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.633769image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:44.988147image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.302639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.589189image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:48.960521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:50.619085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.307581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.802579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:55.198838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:56.806535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:42.415039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:43.763424image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:45.101842image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:46.420316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:47.719840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:49.086185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:50.798604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:52.444204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:53.939236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:45:55.338463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-06-07T17:45:56.958129image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-07T17:45:57.346092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

codecountries_frproduct_namebrandsadditives_nenergy_100gsalt_100gsodium_100gfiber_100gsugars_100gfat_100gsaturated_fat_100gcholesterol_100gnutrition_grade_frnutrition_score_uk_100gnutrition_score_fr_100g
00000000004530États-UnisBanana Chips Sweetened (Whole)NaN0.02243.00.000000.0003.614.2928.5728.570.018d14.00000014.000000
10000000004559États-UnisPeanutsTorn & Glasser0.01941.00.635000.2507.117.8617.860.000.000b0.0000000.000000
20000000016087États-UnisOrganic Salted Nut MixGrizzlies0.02540.01.224280.4827.13.5757.145.360.000d12.00000012.000000
30000000016094États-UnisOrganic PolentaBob's Red Mill0.01552.00.000000.0005.70.001.430.000.000a5.3447335.323729
40000000016100États-UnisBreadshop Honey Gone Nuts GranolaUnfi0.01933.00.000000.0007.711.5418.271.920.000a8.0122077.898541
50000000016117États-UnisOrganic Long Grain White RiceLundberg0.01490.00.000000.0000.00.000.000.000.000a6.8273556.809392
60000000016124États-UnisOrganic MuesliDaddy's Muesli2.01833.00.139700.0559.415.6218.754.690.000c7.0000007.000000
70000000016193États-UnisOrganic Dark Chocolate MinisEqual Exchange0.02406.00.000000.0007.542.5037.5022.500.000a18.27775418.060035
80000000016513États-UnisOrganic Sunflower OilNapa Valley Naturals0.03586.00.000000.0000.00.00100.007.140.000a18.82227917.765301
90000000016612États-UnisOrganic Adzuki BeansUnfi0.01393.00.000000.00012.50.001.040.000.000a2.8747132.929574
codecountries_frproduct_namebrandsadditives_nenergy_100gsalt_100gsodium_100gfiber_100gsugars_100gfat_100gsaturated_fat_100gcholesterol_100gnutrition_grade_frnutrition_score_uk_100gnutrition_score_fr_100g
2583139780803738782États-UnisOrganic Z BarClif Kid1.01393.00.952500.3750008.330.569.722.780.0d11.00000011.000000
2583149782211109758FranceVerrine Cheescake MyrtilleKayser-1.01084.00.290000.1141730.010.500.0012.000.0d16.00000016.000000
2583159782401029101FranceFiche BrevetHatier-1.04.010.000003.93700810.01.000.001.000.0b0.0000000.000000
2583169787461062105États-UnisNatural CassavaIndustria De Casabe Paul0.01477.00.030480.0120004.72.350.000.000.0a-1.000000-1.000000
2583179836654056565États-UnisRaspados Ice BarsJarritos, The Jel Sert Company8.0368.00.045720.0180000.019.300.000.000.0a7.1390067.568028
2583189847548283004FranceTartines craquantes bio au sarrasinLe Pain des fleurs-1.01643.00.680000.2677175.92.602.800.600.0a-4.000000-4.000000
258319989898SuisseTest NF AppNaN0.0569.01.100000.4330711.19.6031.000.000.0a6.6610936.888682
2583209900000000233FranceAmandesBiosic-1.02406.00.100000.03937012.23.890.003.730.0b0.0000000.000000
25832199111250FranceThé vert Earl greyLobodis0.021.00.025400.0100000.20.500.200.200.0c0.0000002.000000
258322999990026839États-UnisSugar Free Drink Mix, Peach TeaMarket Pantry7.02092.00.000000.0000000.00.000.000.000.0a9.7482409.501761